{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.io as sio\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import itertools\n",
    "from collections import defaultdict\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats01s = sio.loadmat('16qubit_data/stats_0.mat')['stats01s']\n",
    "measure_fids = sio.loadmat('16qubit_data/measure_fids.mat')['measure_fids']"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# typical correction and get raw value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def findIndex(qNum, qidx):\n",
    "    order = 2**(qNum-qidx+1)*np.arange(0, 2**(qidx-1))\n",
    "    index0 = np.reshape(order, [len(order), 1]) + np.arange(0, 2**(qNum-qidx))\n",
    "    index0 = np.reshape(index0, 2**(qNum-1))\n",
    "    index1 = index0 + 2**(qNum-qidx)\n",
    "    return np.int32(index0), np.int32(index1)\n",
    "def nchoosek(qnum, n=1):\n",
    "    c = []\n",
    "    for i in itertools.combinations(range(qnum),n):\n",
    "        c.append(list(i))\n",
    "    return c\n",
    "qnum=16\n",
    "_basis = np.zeros([2**qnum,qnum])\n",
    "for i in range(qnum):\n",
    "    index0,index1 = findIndex(qnum,i+1)\n",
    "    _basis[index1,i] = 1\n",
    "ind_occ = nchoosek(qnum=qnum,n=qnum//2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dim0 = stats01s.shape\n",
    "qnum=dim0[2]\n",
    "probs = []\n",
    "probIndexs = []\n",
    "binCoeff = 2**np.arange(dim0[2]-1, -1, -1)\n",
    "probIndexAll = np.arange(2**dim0[2])\n",
    "for ii in range(dim0[1]):\n",
    "    pCounter = np.zeros(2**dim0[2], dtype=int)\n",
    "    for jj in range(dim0[0]):\n",
    "        pCounter[np.sum(stats01s[jj,ii,:] * binCoeff)] += 1\n",
    "    probIndex = probIndexAll[pCounter>0]\n",
    "    count = pCounter[pCounter>0]\n",
    "    probs.append(count)\n",
    "    probIndexs.append(probIndex)\n",
    "probs=np.asarray(probs,dtype=object)\n",
    "probIndexs=np.asarray(probIndexs,dtype=object)\n",
    "probs_raw=np.zeros([dim0[1],2**qnum])\n",
    "for i in range(dim0[1]):\n",
    "    probs_raw[i,probIndexs[i]]=probs[i]/dim0[0]\n",
    "probs = probs_raw\n",
    "probIndexs = 0\n",
    "for ii in range(qnum):\n",
    "    f0 = measure_fids[ii][0]\n",
    "    f1 = measure_fids[ii][1]\n",
    "    corrMat = np.matrix([[f0, 1 - f1], [1 - f0, f1]])\n",
    "    corrMat = corrMat.I\n",
    "    indexP0, indexP1 = findIndex(qnum, ii+1)\n",
    "    probsCorr = np.copy(probs)\n",
    "    probsCorr[:,indexP0] = probs[:,indexP0]*corrMat[0, 0] + probs[:,indexP1] * corrMat[0, 1]\n",
    "    probsCorr[:,indexP1] = probs[:,indexP0]*corrMat[1, 0] + probs[:,indexP1] * corrMat[1, 1]\n",
    "    probs = probsCorr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ini_state = np.sum(stats01s[:,0,:],axis=0)/dim0[0]\n",
    "ini_state = np.array([1 if i>0.5 else 0 for i in ini_state])\n",
    "hamming_raw = np.zeros([dim0[1],qnum+1])\n",
    "for i in range(dim0[0]):\n",
    "    hamming_raw_tmp = np.array(np.sum(np.abs(stats01s[i,:,:]-ini_state),axis=1),dtype=int)\n",
    "    for j in range(dim0[1]):\n",
    "        hamming_raw[j,hamming_raw_tmp[j]]+=1\n",
    "hamming_raw = hamming_raw/dim0[0]\n",
    "psi_ini = np.array(ini_state)\n",
    "psis_temp=np.array(np.sum(np.abs(_basis-psi_ini),axis=1),dtype=np.int32)\n",
    "hammingdex=[np.where(psis_temp==i)[0] for i in range(qnum+1)]\n",
    "hamming_corr=np.array([np.sum(probs[:,hammingdex[i]],axis=1) for i in range(qnum+1)]).transpose(1,0)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# sw_rr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "meas_mats, meas_mats_inv = [], []\n",
    "for qubit in range(qnum):\n",
    "    meas_mat = np.array([[\n",
    "        measure_fids[qubit][0], 1-measure_fids[qubit][1]],\n",
    "        [1-measure_fids[qubit][0], measure_fids[qubit][1]]\n",
    "    ])\n",
    "    meas_mats.append(meas_mat)\n",
    "    meas_mats_inv.append(np.linalg.inv(meas_mat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "i=5\n",
    "stats_num =dim0[0]\n",
    "stats_counts = {}\n",
    "stats_strs_tmp = []\n",
    "stats_strs_type_tmp = []\n",
    "for j in range(stats_num):\n",
    "    stat_str = ''.join([str(ii) for ii in stats01s[j,i,:]])\n",
    "    stats_strs_tmp.append(stat_str)\n",
    "    if stat_str not in stats_strs_type_tmp:\n",
    "        stats_strs_type_tmp.append(stat_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats_counts = dict(zip(stats_strs_type_tmp, [stats_strs_tmp.count(stat_str) for stat_str in stats_strs_type_tmp]))\n",
    "stats_probs = {\n",
    "    basis: count / stats_num\n",
    "    for basis, count in stats_counts.items()\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 21%|██        | 81/387 [00:07<00:27, 11.31it/s]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m/Users/siwei/workspace/readout_error_mitigation/benchmark_sw_rr_new.ipynb 单元格 11\u001b[0m in \u001b[0;36m<cell line: 51>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/siwei/workspace/readout_error_mitigation/benchmark_sw_rr_new.ipynb#X25sZmlsZQ%3D%3D?line=44'>45</a>\u001b[0m     rm_prob \u001b[39m=\u001b[39m {\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/siwei/workspace/readout_error_mitigation/benchmark_sw_rr_new.ipynb#X25sZmlsZQ%3D%3D?line=45'>46</a>\u001b[0m         basis: value \u001b[39m/\u001b[39m sum_prob\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/siwei/workspace/readout_error_mitigation/benchmark_sw_rr_new.ipynb#X25sZmlsZQ%3D%3D?line=46'>47</a>\u001b[0m         \u001b[39mfor\u001b[39;00m basis, value \u001b[39min\u001b[39;00m rm_prob\u001b[39m.\u001b[39mitems()\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/siwei/workspace/readout_error_mitigation/benchmark_sw_rr_new.ipynb#X25sZmlsZQ%3D%3D?line=47'>48</a>\u001b[0m     }\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/siwei/workspace/readout_error_mitigation/benchmark_sw_rr_new.ipynb#X25sZmlsZQ%3D%3D?line=48'>49</a>\u001b[0m     \u001b[39mreturn\u001b[39;00m rm_prob\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/siwei/workspace/readout_error_mitigation/benchmark_sw_rr_new.ipynb#X25sZmlsZQ%3D%3D?line=50'>51</a>\u001b[0m rm_prob \u001b[39m=\u001b[39m sw_rm(qnum, stats_counts, meas_mats_inv, threshold \u001b[39m=\u001b[39;49m stats_num \u001b[39m*\u001b[39;49m \u001b[39m1e-15\u001b[39;49m)\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/siwei/workspace/readout_error_mitigation/benchmark_sw_rr_new.ipynb#X25sZmlsZQ%3D%3D?line=51'>52</a>\u001b[0m rm_prob \u001b[39m=\u001b[39m {\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/siwei/workspace/readout_error_mitigation/benchmark_sw_rr_new.ipynb#X25sZmlsZQ%3D%3D?line=52'>53</a>\u001b[0m     \u001b[39mbin\u001b[39m(basis)\u001b[39m.\u001b[39mreplace(\u001b[39m'\u001b[39m\u001b[39m0b\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39m'\u001b[39m): value\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/siwei/workspace/readout_error_mitigation/benchmark_sw_rr_new.ipynb#X25sZmlsZQ%3D%3D?line=53'>54</a>\u001b[0m     \u001b[39mfor\u001b[39;00m basis, value \u001b[39min\u001b[39;00m rm_prob\u001b[39m.\u001b[39mitems()\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/siwei/workspace/readout_error_mitigation/benchmark_sw_rr_new.ipynb#X25sZmlsZQ%3D%3D?line=54'>55</a>\u001b[0m }\n",
      "\u001b[1;32m/Users/siwei/workspace/readout_error_mitigation/benchmark_sw_rr_new.ipynb 单元格 11\u001b[0m in \u001b[0;36msw_rm\u001b[0;34m(qnum, stats_counts, meas_mats_inv, threshold)\u001b[0m\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/siwei/workspace/readout_error_mitigation/benchmark_sw_rr_new.ipynb#X25sZmlsZQ%3D%3D?line=31'>32</a>\u001b[0m             \u001b[39m# if next_value < threshold and next_value > -threshold:\u001b[39;00m\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/siwei/workspace/readout_error_mitigation/benchmark_sw_rr_new.ipynb#X25sZmlsZQ%3D%3D?line=32'>33</a>\u001b[0m             \u001b[39m#     continue\u001b[39;00m\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/siwei/workspace/readout_error_mitigation/benchmark_sw_rr_new.ipynb#X25sZmlsZQ%3D%3D?line=34'>35</a>\u001b[0m             basis \u001b[39m=\u001b[39m basis \u001b[39m<<\u001b[39m \u001b[39m1\u001b[39m \u001b[39m|\u001b[39m local_basis\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/siwei/workspace/readout_error_mitigation/benchmark_sw_rr_new.ipynb#X25sZmlsZQ%3D%3D?line=35'>36</a>\u001b[0m             next_basis_values\u001b[39m.\u001b[39mappend([basis, next_value])\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/siwei/workspace/readout_error_mitigation/benchmark_sw_rr_new.ipynb#X25sZmlsZQ%3D%3D?line=37'>38</a>\u001b[0m     now_basis_values \u001b[39m=\u001b[39m next_basis_values\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/siwei/workspace/readout_error_mitigation/benchmark_sw_rr_new.ipynb#X25sZmlsZQ%3D%3D?line=39'>40</a>\u001b[0m \u001b[39mfor\u001b[39;00m basis, value \u001b[39min\u001b[39;00m now_basis_values:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "\n",
    "# from functools import lru_cache\n",
    "\n",
    "# def sw_rm(qnum, stats_counts, meas_mats_inv, threshold = 1e-10): \n",
    "#     def transform_(qubit, local_basis):\n",
    "#         local_vec = np.zeros(2)\n",
    "#         local_vec[local_basis] = 1\n",
    "#         local_vec = meas_mats_inv[qubit] @ local_vec\n",
    "#         return local_vec  # new_basis_value\n",
    "\n",
    "#     # qubits = list(range(1, qnum))\n",
    "#     rm_prob = defaultdict(float)\n",
    "#     for basis, count in tqdm(stats_counts.items()):\n",
    "#         basis = [int(c) for c in basis]\n",
    "#         local_vecs = []\n",
    "#         for qubit in range(qnum):\n",
    "#             local_vecs.append(transform_(qubit, basis[qubit]))\n",
    "\n",
    "#         now_basis_values = [\n",
    "#             [local_basis, local_value*count]\n",
    "#             for local_basis, local_value in enumerate(local_vecs[0])\n",
    "#         ]\n",
    "\n",
    "#         for qubit in range(1, qnum):\n",
    "#             next_basis_values = []\n",
    "            \n",
    "#             for local_basis, local_value in enumerate(local_vecs[qubit]):\n",
    "#                 for basis, value in now_basis_values:\n",
    "                    \n",
    "#                     next_value = value * local_value\n",
    "                    \n",
    "#                     # if next_value < threshold and next_value > -threshold:\n",
    "#                     #     continue\n",
    "\n",
    "#                     basis = basis << 1 | local_basis\n",
    "#                     next_basis_values.append([basis, next_value])\n",
    "\n",
    "#             now_basis_values = next_basis_values\n",
    "\n",
    "#         for basis, value in now_basis_values:\n",
    "#             rm_prob[basis] += value\n",
    "\n",
    "\n",
    "#     sum_prob = sum(rm_prob.values())\n",
    "#     rm_prob = {\n",
    "#         basis: value / sum_prob\n",
    "#         for basis, value in rm_prob.items()\n",
    "#     }\n",
    "#     return rm_prob\n",
    "\n",
    "# rm_prob = sw_rm(qnum, stats_counts, meas_mats_inv, threshold = stats_num * 1e-15)\n",
    "# rm_prob = {\n",
    "#     bin(basis).replace('0b', ''): value\n",
    "#     for basis, value in rm_prob.items()\n",
    "# }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 387/387 [00:29<00:00, 13.30it/s]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# from functools import lru_cache\n",
    "\n",
    "# def sw_rm(qnum, stats_counts, meas_mats_inv, threshold = 1e-10): \n",
    "#     # @lru_cache\n",
    "#     def transform_(qubit, local_basis):\n",
    "#         local_vec = np.zeros(2)\n",
    "#         local_vec[local_basis] = 1\n",
    "#         local_vec = meas_mats_inv[qubit] @ local_vec\n",
    "#         return local_vec  # new_basis_value\n",
    "\n",
    "#     # qubits = list(range(1, qnum))\n",
    "#     rm_prob = defaultdict(float)\n",
    "#     for basis, count in tqdm(stats_counts.items()):\n",
    "#         basis = [int(c) for c in basis]\n",
    "#         local_vecs = []\n",
    "#         for qubit in range(qnum):\n",
    "#             local_vecs.append(transform_(qubit, basis[qubit]))\n",
    "\n",
    "#         now_basis_values = [\n",
    "#             [local_basis, local_value*count]\n",
    "#             for local_basis, local_value in enumerate(local_vecs[0])\n",
    "#         ]\n",
    "\n",
    "#         for qubit in range(1, qnum):\n",
    "#             next_basis_values = []\n",
    "            \n",
    "#             for local_basis, local_value in enumerate(local_vecs[qubit]):\n",
    "#                 for basis, value in now_basis_values:\n",
    "                    \n",
    "#                     next_value = value * local_value\n",
    "                    \n",
    "#                     # if next_value < threshold and next_value > -threshold:\n",
    "#                     #     continue\n",
    "\n",
    "#                     basis = basis << 1 | local_basis\n",
    "#                     next_basis_values.append([basis, next_value])\n",
    "\n",
    "#             now_basis_values = next_basis_values\n",
    "\n",
    "#         for basis, value in now_basis_values:\n",
    "#             rm_prob[basis] += value\n",
    "\n",
    "\n",
    "#     sum_prob = sum(rm_prob.values())\n",
    "#     rm_prob = {\n",
    "#         basis: value / sum_prob\n",
    "#         for basis, value in rm_prob.items()\n",
    "#     }\n",
    "#     return rm_prob\n",
    "\n",
    "# rm_prob = sw_rm(qnum, stats_counts, meas_mats_inv, threshold = stats_num * 1e-15)\n",
    "# rm_prob = {\n",
    "#     bin(basis).replace('0b', ''): value\n",
    "#     for basis, value in rm_prob.items()\n",
    "# }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 387/387 [00:00<00:00, 4596.65it/s]\n"
     ]
    }
   ],
   "source": [
    "def sw_rm(qnum, stats_counts, meas_mats_inv, threshold=1e-10):\n",
    "    all_local_vecs = np.zeros(shape=(qnum, 2, 2))\n",
    "\n",
    "    for qubit in range(qnum):\n",
    "        for local_basis in (0, 1):\n",
    "            local_vec = np.zeros(2)\n",
    "            local_vec[local_basis] = 1\n",
    "            local_vec = meas_mats_inv[qubit] @ local_vec\n",
    "            all_local_vecs[qubit][local_basis] = local_vec\n",
    "\n",
    "    rm_prob = defaultdict(float)\n",
    "    for basis, count in tqdm(stats_counts.items()):\n",
    "        basis = [int(c) for c in basis]\n",
    "\n",
    "        # now_basis_values = [\n",
    "        #     [local_basis, local_value*count]\n",
    "        #     for local_basis, local_value in enumerate(all_local_vecs[0][basis[0]])\n",
    "        # ]\n",
    "        now_basis_values = [\n",
    "            [[local_basis], local_value*count]\n",
    "            for local_basis, local_value in enumerate(all_local_vecs[0][basis[0]])\n",
    "        ]\n",
    "\n",
    "        for qubit in range(1, qnum):\n",
    "            next_basis_values = []\n",
    "            for local_basis, local_value in enumerate(all_local_vecs[qubit][basis[qubit]]):\n",
    "                for now_basis, value in now_basis_values:\n",
    "                    next_value = value * local_value\n",
    "\n",
    "                    if next_value < threshold and next_value > -threshold:\n",
    "                        continue\n",
    "\n",
    "                    now_basis = list(now_basis)\n",
    "                    now_basis.append(local_basis)\n",
    "                    next_basis_values.append(\n",
    "                        [now_basis, next_value])\n",
    "                    \n",
    "                    # next_basis_values.append(\n",
    "                        # [now_basis << 1 | local_basis, next_value])\n",
    "\n",
    "            now_basis_values = next_basis_values\n",
    "\n",
    "        for basis, value in now_basis_values:\n",
    "            rm_prob[''.join([str(c) for c in basis])] += value\n",
    "            # rm_prob[basis] += value\n",
    "\n",
    "    sum_prob = sum(rm_prob.values())\n",
    "    rm_prob = {\n",
    "        basis: value / sum_prob\n",
    "        for basis, value in rm_prob.items()\n",
    "    }\n",
    "    return rm_prob\n",
    "\n",
    "rm_prob = sw_rm(qnum, stats_counts, meas_mats_inv, threshold = stats_num * 1e-5)\n",
    "# rm_prob = {\n",
    "#     bin(basis).replace('0b', ''): value\n",
    "#     for basis, value in rm_prob.items()\n",
    "# }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "hamming_sw_rr=np.zeros([qnum+1])\n",
    "ini_state_str = ''.join(['1' if ini_i == 1 else '0' for ini_i in ini_state])\n",
    "for q_str in rm_prob.keys():\n",
    "    hamming_sw_rr[sum(c1 != c2 for c1, c2 in zip(q_str, ini_state_str))]+=rm_prob[q_str]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'hamming probs')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "LABEL_COLORS_1 = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink']\n",
    "tlist = np.arange(dim0[1])\n",
    "select_t = [5,30,100]\n",
    "plt.figure(figsize=(9,6),dpi=100)\n",
    "for color_i,i in enumerate(select_t):\n",
    "    plt.plot(np.arange(0,qnum+1,1),hamming_raw[i,:],'o--',color=LABEL_COLORS_1[color_i],\n",
    "            markerfacecolor='none',label=f'Raw t = {tlist[i]}')\n",
    "    plt.plot(np.arange(0,qnum+1,1),hamming_corr[i,:],'*-',\n",
    "            color=LABEL_COLORS_1[color_i],\n",
    "            markerfacecolor='none',label=f'corrected t = {tlist[i]}')\n",
    "plt.plot(np.arange(0,qnum+1,1),hamming_sw_rr,'D-',alpha=0.5,\n",
    "            color=LABEL_COLORS_1[color_i],\n",
    "            markerfacecolor='none',label=f'sw corrected t = {tlist[i]}')\n",
    "plt.legend(ncol=len(select_t))\n",
    "plt.xlabel('hamming distance')\n",
    "plt.ylabel('hamming probs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
